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Title: TOBB ETU at CheckThat! 2022: Detecting Attention-Worthy and Harmful Tweets and Check-Worthy Claims
Authors: Eyuboglu A.B.
Arslan M.B.
Sonmezer E.
Kutlu M.
Keywords: Attention-worthy tweets
Factual Claims
Harmful tweets
Attention-worthy tweet
Factual claim
Fine tuning
Harmful tweet
Training data
Transformer modeling
Issue Date: 2022
Publisher: CEUR-WS
Abstract: In this paper, we present our participation in CLEF 2022 CheckThat! Lab’s Task 1 on detecting check-worthy and verifiable claims and attention-worthy and harmful tweets. We participated in all subtasks of Task1 for Arabic, Bulgarian, Dutch, English, and Turkish datasets. We investigate the impact of fine-tuning various transformer models and how to increase training data size using machine translation. We also use feed-forward networks with the Manifold Mixup regularization for the respective tasks. We are ranked first in detecting factual claims in Arabic and harmful tweets in Dutch. In addition, we are ranked second in detecting check-worthy claims in Arabic and Bulgarian. © 2022 Copyright for this paper by its authors.
Description: 2022 Conference and Labs of the Evaluation Forum, CLEF 2022 -- 5 September 2022 through 8 September 2022 -- -- 181762
ISSN: 1613-0073
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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